Course Name: SIGNALS AND SYSTEMS Course Code: EEL202 Scheme: (3-1-0-Credits -4)
Objective of the course:
The subject deals with various methods of analysis for continuous time and discrete time systems in time domain and frequency domain
Being a basic course, students need to master this subject well and associate its basic concepts in order to become competent engineers
Syllabus:
Elements of Signal Space Theory: Different Types of Signals, Linearity, Time Invariance and Causality, Impulse Sequence, Impulse Functions and Other Singularity Functions.
Convolution: Convolution Sum, Convolution Integral and Their Evaluation, Time Domain Representation and Analysis, of LTI Systems Based on Convolution and Differential Equations.
Multi Input-Output Discrete and Continuous Systems: State Model Representation, Solution of State, Equations, State Transition Matrix.
Transform Domain Considerations: Laplace Transforms and Z-Transforms, Application of Transforms to Discrete and Continuous Systems Analysis, Transfer Function, Block Diagram Representation, and DFT.
Fourier series and Fourier Transform: Sampling Theorem, Discrete Fourier Transform (DFT), Estimating Fourier Transform Using (DFT).
Course Outcomes:
Students are able to 1. Know basics of signal space theory. 2. Understand convolution sum of two signals. 3. Appreciate the concepts of state space representation. 4. Apply different transform for discrete and continuous analysis.
Welcome to Signal and System e-Classroom
Table of Contents
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Course Name: SIGNALS AND SYSTEMSCourse Code: EEL202
Scheme: (3-1-0-Credits -4)
Objective of the course:
Syllabus:
Elements of Signal Space Theory: Different Types of Signals, Linearity, Time Invariance and Causality, Impulse Sequence, ImpulseFunctions and Other Singularity Functions.
Convolution: Convolution Sum, Convolution Integral and Their Evaluation, Time Domain Representation and Analysis, of LTI Systems
Based on Convolution and Differential Equations.
Multi Input-Output Discrete and Continuous Systems: State Model Representation, Solution of State, Equations, State Transition Matrix.
Transform Domain Considerations: Laplace Transforms and Z-Transforms, Application of Transforms to Discrete and Continuous
Systems Analysis, Transfer Function, Block Diagram Representation, and DFT.
Fourier series and Fourier Transform: Sampling Theorem, Discrete Fourier Transform (DFT), Estimating Fourier Transform Using (DFT).
Course Outcomes:
Students are able to1. Know basics of signal space theory.
2. Understand convolution sum of two signals.
3. Appreciate the concepts of state space representation.
4. Apply different transform for discrete and continuous analysis.